1. Field of the Invention
This invention relates to market demand estimation, and particularly to estimation of demand for information technology (IT) services.
2. Description of Background
In IT services markets, demand for a service offering is generally governed by a price and quality of the service offering. Customers have different profiles and respond differently to price and quality or levels of service offerings. The level of service offering that a firm can offer depends upon its capacity and the demand. Demand variability due to changing market conditions can be high and firms often need to act quickly to maintain a level of capacity sufficient to provide a level of service offering to which they have committed. Therefore, there is an interdependence between demand, price, quality of service, and capacity. Accordingly, decisions regarding service, pricing and capacity planning are inter-twined.
An objective of pricing decisions is to set a price associated with a service level to achieve business objectives or goals, such as revenue increase and customer base expansion for example. An objective of capacity planning decisions is to maintain a level of capacity to meet service level agreements. Both capacity planning and pricing decisions require knowledge of demand.
In an on-demand market paradigm, the demand for a specific service level is uncertain because customers have the flexibility to select and pay for services as and when needed, without any long-term fixed cost obligation. IT services markets are particularly uncertain because firms are constantly exploring markets for new services and experimenting with different service offerings.
Present approaches estimate demand as a function only of price and can be classified as exploratory and non-exploratory. The exploratory and non-exploratory approaches can be further classified as parametric or non-parametric. Parametric approaches model demand distribution using some known family of probability distribution function and estimate the unknown parameters of the demand distribution. Non-parametric approaches estimate the empirical distribution of demand. Both parametric and non parametric approaches can update the estimated quantities in a static or dynamic manner.
Non-exploratory approaches for demand estimation are typically suited for products that are well established in the markets and have demand that is predictable from historical data. For products having demand with volatility, such as subject to fluctuations of changing market conditions for example, non-exploratory approaches may fail to perform well.
Exploratory approaches include firm experimentation with different service offerings to simultaneously learn the demand while doing business. A typical exploratory approach includes setting a price, and observing customer response to this price. The observation is used to update knowledge about the unknown demand. However, this requires offering each of the different price levels a sufficient number of times so as to get a close estimate of a curve that describes the demand. The drawback of such approaches is that the demand learning is at the expense of foregone profits. Further, these approaches lack interaction of service quality, are not scalable, and are time consuming. Accordingly, there is a need in the art for a demand estimation arrangement that overcomes these drawbacks.